elif¶

In [ ]:
In [19]:
a=int(input("Enter the number: "))
l=[500,200,100,10,5,1]
x={}
for i in l:
    x[i]=a//i
    a=a%i
print(x)

Enter the number: 3475
{500: 6, 200: 2, 100: 0, 10: 7, 5: 1, 1: 0}
In [ ]:
In [18]:
x=input("Enter the string: ")
a=''
b=''
for i in x:
    if i.isalnum():
        a=a+i
    else:
        b=b+a[::-1]+i
        a=''
print(b+a[::-1])

Enter the string: 2233/46543/34thdb*hef56433
3322/34564/bdht43*33465feh
In [22]:
x=int(input("Enter the price: "))
if x>2000:
    print((x/100)*20)
elif x>1500 and x<=2000:
    print((x/100)*18)
elif x>1200 and x<=1500:
    print((x/100)*15)
elif x>800 and x<=1200:
    print((x/100)*10)
elif x>500 and x<=800:
    print((x/100)*7)
elif x>300 and x<=500:
    print((x/100)*5)
elif x<=300:
    print(x)

Enter the price: 2500
500.0
In [23]:
s1={1,2,3,4}
s2={2,3,4}
s1.union(s2)
Out[23]:
{1, 2, 3, 4}
In [24]:
s1 | s2
Out[24]:
{1, 2, 3, 4}
In [25]:
s1 - s2
Out[25]:
{1}
In [26]:
s1 & s2
Out[26]:
{2, 3, 4}
In [27]:
d3={'a':[1,2],'b':[3,4]}
d4={'b':[30,40],'c':[5,6]}
In [28]:
d3.values()
Out[28]:
dict_values([[1, 2], [3, 4]])
In [31]:
d1={'a':1,'b':2,'c':3}
d2={'b':2,'c':30,'d':4}
In [42]:
d={}
for k in d1.keys() & d2.keys():
    d[k]=d1[k],d2[k]
print(d)

{'b': (2, 2), 'c': (3, 30)}
In [47]:
d1={'a':1,'b':2,'c':3}
d2={'b':2,'c':30,'d':4}
In [50]:
d={key:(d1[key],d2[key])for key in d1.keys() & d2.keys()}
print(d)

{'b': (2, 2), 'c': (3, 30)}
In [1]:
d1={'a':1,'b':2,'c':3,'d':4}
d2={'b':2,'c':30,'a':4,'e':5}
In [1]:
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [2]:
cancer_df=pd.read_csv('C:\\Users\\baliy\\Downloads\\cancer.csv')
In [3]:
cancer_df
Out[3]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.30010 0.14710 0.2419 0.07871 ... 17.33 184.60 2019.0 0.16220 0.66560 0.7119 0.2654 0.4601 0.11890 0
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.08690 0.07017 0.1812 0.05667 ... 23.41 158.80 1956.0 0.12380 0.18660 0.2416 0.1860 0.2750 0.08902 0
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.19740 0.12790 0.2069 0.05999 ... 25.53 152.50 1709.0 0.14440 0.42450 0.4504 0.2430 0.3613 0.08758 0
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.24140 0.10520 0.2597 0.09744 ... 26.50 98.87 567.7 0.20980 0.86630 0.6869 0.2575 0.6638 0.17300 0
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.19800 0.10430 0.1809 0.05883 ... 16.67 152.20 1575.0 0.13740 0.20500 0.4000 0.1625 0.2364 0.07678 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 21.56 22.39 142.00 1479.0 0.11100 0.11590 0.24390 0.13890 0.1726 0.05623 ... 26.40 166.10 2027.0 0.14100 0.21130 0.4107 0.2216 0.2060 0.07115 0
565 20.13 28.25 131.20 1261.0 0.09780 0.10340 0.14400 0.09791 0.1752 0.05533 ... 38.25 155.00 1731.0 0.11660 0.19220 0.3215 0.1628 0.2572 0.06637 0
566 16.60 28.08 108.30 858.1 0.08455 0.10230 0.09251 0.05302 0.1590 0.05648 ... 34.12 126.70 1124.0 0.11390 0.30940 0.3403 0.1418 0.2218 0.07820 0
567 20.60 29.33 140.10 1265.0 0.11780 0.27700 0.35140 0.15200 0.2397 0.07016 ... 39.42 184.60 1821.0 0.16500 0.86810 0.9387 0.2650 0.4087 0.12400 0
568 7.76 24.54 47.92 181.0 0.05263 0.04362 0.00000 0.00000 0.1587 0.05884 ... 30.37 59.16 268.6 0.08996 0.06444 0.0000 0.0000 0.2871 0.07039 1

569 rows × 31 columns

In [11]:
sns.scatterplot(x='mean area',y='mean smoothness',hue='worst perimeter',data=cancer_df)
Out[11]:
<AxesSubplot:xlabel='mean area', ylabel='mean smoothness'>
In [13]:
sns.countplot(cancer_df['target'])

C:\Users\baliy\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[13]:
<AxesSubplot:xlabel='target', ylabel='count'>
In [25]:
sns.pairplot(cancer_df,hue="target",vars=['mean radius','mean texture','mean perimeter','mean area','mean smoothness','mean compactness','mean concavity','mean concave points','mean symmetry','mean fractal dimension','worst texture','worst perimeter','worst area','worst smoothness','worst compactness','worst concavity','worst concave points','worst symmetry','worst fractal dimension','target'])
Out[25]:
<seaborn.axisgrid.PairGrid at 0x20083643490>
In [11]:
sns.pairplot(cancer_df,hue="target",vars=['mean radius','mean texture','mean perimeter','mean area'])
Out[11]:
<seaborn.axisgrid.PairGrid at 0x1d9a1f00f10>
In [ ]:
In [8]:
sns.pairplot(cancer_df,hue="target",vars=['mean radius','mean texture','mean perimeter','mean area','mean compactness'])
Out[8]:
<seaborn.axisgrid.PairGrid at 0x1b30b9ced30>
In [17]:
plt.figure(figsize=(20,10))
sns.heatmap(cancer_df.corr(),annot=True)
Out[17]:
<AxesSubplot:>
In [ ]:

Plot scatter plot between mean area and mean smoothness¶

In [33]:
sns.distplot(cancer_df['mean radius'],bins=15,color='black')

C:\Users\baliy\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
Out[33]:
<AxesSubplot:xlabel='mean radius', ylabel='Density'>
In [32]:
sns.displot(cancer_df['mean radius'],bins=15,color='black')
Out[32]:
<seaborn.axisgrid.FacetGrid at 0x23bee96e940>
In [18]:
sns.distplot(cancer_df['mean radius'],bins=5,color='black')

C:\Users\baliy\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
Out[18]:
<AxesSubplot:xlabel='mean radius', ylabel='Density'>
In [ ]:
In [36]:
tips=sns.load_dataset('tips')
In [37]:
tips
Out[37]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
... ... ... ... ... ... ... ...
239 29.03 5.92 Male No Sat Dinner 3
240 27.18 2.00 Female Yes Sat Dinner 2
241 22.67 2.00 Male Yes Sat Dinner 2
242 17.82 1.75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2

244 rows × 7 columns

In [39]:
sns.relplot(x='total_bill',y='tip',data=tips)
Out[39]:
<seaborn.axisgrid.FacetGrid at 0x206dfba0af0>
In [41]:
sns.relplot(x='total_bill',y='tip',data=tips,hue='time')
Out[41]:
<seaborn.axisgrid.FacetGrid at 0x206dfd07100>
In [42]:
sns.relplot(x='total_bill',y='tip',data=tips,hue='smoker',style='time')
Out[42]:
<seaborn.axisgrid.FacetGrid at 0x206dfae8c40>
In [44]:
sns.relplot(x='total_bill',y='tip',data=tips,size='size',sizes=(25,250))
Out[44]:
<seaborn.axisgrid.FacetGrid at 0x206e0016ee0>
In [27]:
df=pd.DataFrame(dict(time=np.arange(500),value=randn(500),cumsum()))

  Input In [27]
    df=pd.DataFrame(dict(time=np.arange(500),value=randn(500),cumsum()))
                                                                      ^
SyntaxError: positional argument follows keyword argument
In [28]:
sns.relplot(x='time',y='value',kind='line',data='df')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [28], in <cell line: 1>()
----> 1 sns.relplot(x='time',y='value',kind='line',data='df')

File ~\anaconda3\lib\site-packages\seaborn\_decorators.py:46, in _deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
     36     warnings.warn(
     37         "Pass the following variable{} as {}keyword arg{}: {}. "
     38         "From version 0.12, the only valid positional argument "
   (...)
     43         FutureWarning
     44     )
     45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)

File ~\anaconda3\lib\site-packages\seaborn\relational.py:947, in relplot(x, y, hue, size, style, data, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, units, **kwargs)
    944     kwargs.pop("ax")
    946 # Use the full dataset to map the semantics
--> 947 p = plotter(
    948     data=data,
    949     variables=plotter.get_semantics(locals()),
    950     legend=legend,
    951 )
    952 p.map_hue(palette=palette, order=hue_order, norm=hue_norm)
    953 p.map_size(sizes=sizes, order=size_order, norm=size_norm)

File ~\anaconda3\lib\site-packages\seaborn\relational.py:367, in _LinePlotter.__init__(self, data, variables, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend)
    353 def __init__(
    354     self, *,
    355     data=None, variables={},
   (...)
    361     # the kind of plot to draw, but for the time being we need to set
    362     # this information so the SizeMapping can use it
    363     self._default_size_range = (
    364         np.r_[.5, 2] * mpl.rcParams["lines.linewidth"]
    365     )
--> 367     super().__init__(data=data, variables=variables)
    369     self.estimator = estimator
    370     self.ci = ci

File ~\anaconda3\lib\site-packages\seaborn\_core.py:605, in VectorPlotter.__init__(self, data, variables)
    603 def __init__(self, data=None, variables={}):
--> 605     self.assign_variables(data, variables)
    607     for var, cls in self._semantic_mappings.items():
    608 
    609         # Create the mapping function
    610         map_func = partial(cls.map, plotter=self)

File ~\anaconda3\lib\site-packages\seaborn\_core.py:668, in VectorPlotter.assign_variables(self, data, variables)
    666 else:
    667     self.input_format = "long"
--> 668     plot_data, variables = self._assign_variables_longform(
    669         data, **variables,
    670     )
    672 self.plot_data = plot_data
    673 self.variables = variables

File ~\anaconda3\lib\site-packages\seaborn\_core.py:903, in VectorPlotter._assign_variables_longform(self, data, **kwargs)
    898 elif isinstance(val, (str, bytes)):
    899 
    900     # This looks like a column name but we don't know what it means!
    902     err = f"Could not interpret value `{val}` for parameter `{key}`"
--> 903     raise ValueError(err)
    905 else:
    906 
    907     # Otherwise, assume the value is itself data
    908 
    909     # Raise when data object is present and a vector can't matched
    910     if isinstance(data, pd.DataFrame) and not isinstance(val, pd.Series):

ValueError: Could not interpret value `time` for parameter `x`
In [ ]:
In [ ]:
In [ ]: